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Greedy.Plugin.EIPOD Class Reference

Detailed Description

Plugin for the Greedy.Algorithm class generating a collateral reduced basis space plus interpolation DOFs and a local grid.

This can be used for the empirical interpolation of parametrized functions or operator evaluations.

Definition at line 19 of file EIPOD.m.

Inheritance diagram for Greedy.Plugin.EIPOD:
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Public Member Functions

 EIPOD (SnapshotsGenerator.Cached generator)
 constructor storing the snapshot generator More...
 
function
Uapprox = 
generate_reduced (Greedy.User.IReducedModel rmodel,Greedy.User.IReducedData reduced_data,Greedy.User.IDetailedData detailed_data, U)
 generates a reduced function \(v_{\text{red}}(\mu)\). More...
 
function Greedy.DataTree.Detailed.EILeafNode
detailed_data = 
basis_extension (Greedy.User.IReducedModel rmodel,Greedy.DataTree.Detailed.EILeafNode detailed_data, max_err_seq, mu)
 extends the reduced basis space from a given function \(v_{h}(\mu)\). More...
 
function Greedy.Plugin.EI cop = copy ()
 deep copy of Greedy.Plugin.EI object More...
 
- Public Member Functions inherited from Greedy.Plugin.EI
 EI (SnapshotsGenerator.Cached generator)
 constructor storing the snapshot generator More...
 
function Greedy.DataTree.Detailed.EILeafNode
detailed_data = 
init_basis (Greedy.User.IReducedModel rmodel,ModelData model_data, M_train)
 creates an initial detailed data node storing an initial reduced basis More...
 
function  prepare_reduced_data (Greedy.User.IReducedModel rmodel,Greedy.DataTree.Detailed.EILeafNode detailed_data)
 prepares reduced data that is necessary for the execution of other methods if indicated by needs_preparation. More...
 
function
Uapprox = 
generate_reduced (Greedy.User.IReducedModel rmodel,Greedy.User.IReducedData reduced_data,Greedy.User.IDetailedData detailed_data, U)
 generates a reduced function \(v_{\text{red}}(\mu)\). More...
 
function [
breakloop , reason ] = 
pre_check_for_end (Greedy.User.IReducedModel rmodel,Greedy.DataTree.Detailed.EILeafNode detailed_data)
 checks whether the basis generation process has come to an end. More...
 
function Greedy.DataTree.Detailed.EILeafNode
detailed_data = 
basis_extension (Greedy.User.IReducedModel rmodel,Greedy.DataTree.Detailed.EILeafNode detailed_data, max_err_seq, mu)
 extends the reduced basis space from a given function \(v_{h}(\mu)\). More...
 
- Public Member Functions inherited from Greedy.Plugin.EICommon
 EICommon (SnapshotsGenerator.SpaceOpEvals generator)
 constructor for an EICommon instance More...
 
function [
max_errs ,
max_err_sequence
,
max_mu_index ] = 
error_estimators (IReducedModel rmodel,Greedy.DataTree.Detailed.EILeafNode detailed_data, M_train)
 computes a posteriori error estimators for the reduced simulations for every parameter vector from a given parameter set \({\cal M}_{\text{train}}\). More...
 
function Greedy.DataTree.Detailed.EILeafNode
detailed_data = 
finalize (IReducedModel rmodel,Greedy.DataTree.Detailed.EILeafNode detailed_data)
 function called after the last extension process More...
 
function Greedy.Plugin.EI merged = horzcat (varargin)
 combines an arbitrary number of Greedy.Plugin.EI arguments to a big one, with added SnapshotsGenerator.Cached instances. More...
 
function summed = vertcat (varargin)
 combines an arbitrary number of Greedy.Plugin.EI arguments to a Greedy.Plugin.SummedEI instance. More...
 
- Public Member Functions inherited from Greedy.Plugin.Default
 Default (SnapshotsGenerator.Cached generator)
 constructor for a greedy extension object More...
 
function [
max_errs ,
max_err_sequence
,
max_mu_index ] = 
error_indicators (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data,ParameterSampling.Interface parameter_set, reuse_reduced_data)
 computes error indicators for the reduced simulations for every parameter vector from a given parameter set \({\cal M}_{\text{train}}\). More...
 
function errs = compute_error (Greedy.User.IReducedModel rmodel,Greedy.User.ReducedData reduced_data,Greedy.User.IDetailedData detailed_data)
 computes the "true" error between a reduced and a detailed function \(\| v_h(t^k;\mu) - v_{\text{red}}(t^k;\mu) \|\). More...
 
virtual function [

max_errs ,
max_err_sequence
,
max_mu_index ] = 
error_estimators (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data, M_train)
 computes a posteriori error estimators for the reduced simulations for every parameter vector from a given parameter set \({\cal M}_{\text{train}}\). More...
 
- Public Member Functions inherited from Greedy.Plugin.Interface
 Interface (SnapshotsGenerator.Cached generator)
 constructor for a greedy extension object More...
 
virtual function Greedy.DataTree.Detailed.INode
detailed_data = 
init_basis (Greedy.User.IReducedModel rmodel,ModelData model_data,ParameterSampling.Interface M_train)
 creates an initial detailed data node storing an initial reduced basis More...
 
virtual function  prepare_reduced_data (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data)
 prepares reduced data that is necessary for the execution of other methods if indicated by needs_preparation. More...
 
virtual function [

max_errs ,
max_err_sequence
,
max_mu_index ] = 
error_indicators (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data, parameter_set, reuse_reduced_data)
 computes error indicators for the reduced simulations for every parameter vector from a given parameter set \({\cal M}_{\text{train}}\). More...
 
virtual function
Uapprox = 
generate_reduced (Greedy.User.IReducedModel rmodel,Greedy.User.ReducedData reduced_data,Greedy.User.IDetailedData detailed_data, U)
 generates a reduced function \(v_{\text{red}}(\mu)\). More...
 
virtual function [

breakloop , reason ] = 
pre_check_for_end (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data)
 checks whether the basis generation process has come to an end. More...
 
virtual function Greedy.User.IDetailedData
detailed_data = 
basis_extension (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data, max_err_seq, mu)
 extends the reduced basis space from a given function \(v_{h}(\mu)\). More...
 
virtual function Greedy.User.IDetailedData
detailed_data = 
finalize (Greedy.User.IReducedModel rmodel,Greedy.User.IDetailedData detailed_data)
 function called after the last extension process More...
 

Additional Inherited Members

- Public Attributes inherited from Greedy.Plugin.EI
 A = "[]"
 temporary matrix generated by prepare_reduced_data() More...
 
 stop_Mmax = inf
 maximum number of generated collateral reduced basis vectors. More...
 
 ei_target_error = "approx"
 error mode for the empirical interpolation error More...
 
 target_error_external = false
 boolean flag indicating that the error indicator which is assumed to be minimized is not equal to the ones returned by error_indicators() More...
 
 noof_extensions = 1
 specifies how many collateral reduced basis functions shall be added in each extension step. More...
 
 minimum_residual = 1e-6
 if the residual is below this barrier, the basis extension step is skipped. More...
 
 enable_regularisation = false
 if this regularisation flag is set to true, the collateral reduced basis spaces are forced to have zero mean, enforcing global conservation in case of operator interpolation. More...
 
- Public Attributes inherited from Greedy.Plugin.EICommon
 compute_lebesgue = true
 boolean flag specifying whether after the final extension step the Lebesgue constant shall be computed and stored in the Greedy.DataTree.Detailed.EILeafNode info fields. More...
 
- Public Attributes inherited from Greedy.Plugin.Default
Greedy.User.ReducedData reduced_data = "[]"
 temporary handle to the last object computed by prepare_reduced_data(). More...
 
 needs_preparation
 boolean indicating whether the prepare_reduced_data() method needs to be computed before error_indicators can be computed. More...
 
 indicator_mode = "error"
 string specifying which indicators shall be used by the error_indicators() method. More...
 
 use_l2_error = true
 boolean flag indicating whether the \(L^2(\Omega)\)-norm is used by compute_error(). More...
 
 relative_error = false
 boolean flag specifying whether we want to use the relative error for error_indicators() and compute_error() methods. More...
 
- Public Attributes inherited from Greedy.Plugin.Interface
 id
 a string identifying the basis extension algorithm, should be unique over all instances of Interface implementations. More...
 
 relative_error
 boolean flag specifying whether we want to use the relative error for error_indicators() and compute_error() methods. More...
 
 indicator_mode
 string specifying which indicators shall be used by the error_indicators() method. More...
 
 needs_preparation
 boolean indicating whether the prepare_reduced_data() method needs to be computed before error_indicators can be computed. More...
 
::SnapshotsGenerator.Cached generator
 an object generating possible (high dimension) basis functions
 
- Static Public Attributes inherited from Greedy.Plugin.EI
static const  generated_basis_type = "ei"
 
static const  info_fields
 cell array of field names that shall be copied to the generated Greedy.DataTree.Detailed.EILeafNode instance. More...
 
- Static Public Attributes inherited from Greedy.Plugin.Interface
static const  generated_basis_type
 string specifying the detailed data produced by this basis generation algorithm object. More...
 

Constructor & Destructor Documentation

Greedy.Plugin.EIPOD.EIPOD ( SnapshotsGenerator.Cached  generator)

constructor storing the snapshot generator

Parameters
generatorsnapshot generator

Definition at line 34 of file EIPOD.m.

Member Function Documentation

function Greedy.DataTree.Detailed.EILeafNode detailed_data = Greedy.Plugin.EIPOD.basis_extension ( Greedy.User.IReducedModel  rmodel,
Greedy.DataTree.Detailed.EILeafNode  detailed_data,
  max_err_seq,
  mu 
)

extends the reduced basis space from a given function \(v_{h}(\mu)\).

This generates a new basis function \(\varphi_{n+1} \in {\cal W}_h\) from the sequence of detailed functions \(v_h(t^k,\mu)\) for \(k=0,\ldots,K\) as returned by the SnapshotsGenerator.Cached generator.

Parameters
rmodelan object specifying the basis generation process.
detailed_dataobject defining the basis generation algorithm and storage for storing high dimensional data, i.e. dependent on dimension \(H\). This data is necessary for detailed simulations, construction of online matrices, reduced_data and reconstruction of reduced simulations.
max_err_seqsequence of error indicators as returned as second return argument of error_indicators()
muparamter vector \(\mu\).
Return values
detailed_dataupdated data tree node .

Definition at line 68 of file EIPOD.m.

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function Greedy.Plugin.EI cop = Greedy.Plugin.EIPOD.copy ( )
virtual

deep copy of Greedy.Plugin.EI object

Return values
copcopied object

Reimplemented from Greedy.Plugin.EI.

Definition at line 166 of file EIPOD.m.

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function Uapprox = Greedy.Plugin.EIPOD.generate_reduced ( Greedy.User.IReducedModel  rmodel,
Greedy.User.IReducedData  reduced_data,
Greedy.User.IDetailedData  detailed_data,
  U 
)

generates a reduced function \(v_{\text{red}}(\mu)\).

Note
This function might depend on a previous execution of prepare_reduced_data().
Parameters
rmodelan object specifying the basis generation process. The parameter \(\mu\) for which the error shall be computed must be set by set_mu(rmodel, mu) before.
reduced_dataan object storing all (low-dimensional) reduced matrices and vectors needed for reduced simulations.
detailed_dataobject defining the basis generation algorithm and storage for storing high dimensional data, i.e. dependent on dimension \(H\). This data is necessary for detailed simulations, construction of online matrices, reduced_data and reconstruction of reduced simulations.
Uoptional DOF vector, for example operator evaluations.
Return values
UapproxA sequence of Dof vectors of the functions \(v_{\text{red}}(\cdot;t^k,\mu)\).

Definition at line 45 of file EIPOD.m.

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The documentation for this class was generated from the following file: